Context
The World Happiness Report is a landmark survey of the state of
global happiness. The first report was published in 2012, the second in
2013, the third in 2015, and the fourth in the 2016 Update. The World
Happiness 2017, which ranks 155 countries by their happiness levels, was
released at the United Nations at an event celebrating International Day
of Happiness on March 20th. The report continues to gain global
recognition as governments, organizations and civil society increasingly
use happiness indicators to inform their policy-making decisions.
Leading experts across fields – economics, psychology, survey analysis,
national statistics, health, public policy and more – describe how
measurements of well-being can be used effectively to assess the
progress of nations. The reports review the state of happiness in the
world today and show how the new science of happiness explains personal
and national variations in happiness.
Content
The happiness scores and rankings use data from the Gallup World
Poll. The scores are based on answers to the main life evaluation
question asked in the poll. This question, known as the Cantril ladder,
asks respondents to think of a ladder with the best possible life for
them being a 10 and the worst possible life being a 0 and to rate their
own current lives on that scale. The scores are from nationally
representative samples for the years 2013-2016 and use the Gallup
weights to make the estimates representative. The columns following the
happiness score estimate the extent to which each of six factors –
economic production, social support, life expectancy, freedom, absence
of corruption, and generosity – contribute to making life evaluations
higher in each country than they are in Dystopia, a hypothetical country
that has values equal to the world’s lowest national averages for each
of the six factors. They have no impact on the total score reported for
each country, but they do explain why some countries rank higher than
others.
Inspiration
What countries or regions rank the highest in overall happiness and
each of the six factors contributing to happiness? How did country ranks
or scores change between the 2015 and 2016 as well as the 2016 and 2017
reports? Did any country experience a significant increase or decrease
in happiness?
What is Dystopia?
Dystopia is an imaginary country that has the world’s least-happy
people. The purpose in establishing Dystopia is to have a benchmark
against which all countries can be favorably compared (no country
performs more poorly than Dystopia) in terms of each of the six key
variables, thus allowing each sub-bar to be of positive width. The
lowest scores observed for the six key variables, therefore,
characterize Dystopia. Since life would be very unpleasant in a country
with the world’s lowest incomes, lowest life expectancy, lowest
generosity, most corruption, least freedom and least social support, it
is referred to as “Dystopia,” in contrast to Utopia.
What are the residuals?
The residuals, or unexplained components, differ for each country,
reflecting the extent to which the six variables either over- or
under-explain average 2014-2016 life evaluations. These residuals have
an average value of approximately zero over the whole set of countries.
Figure 2.2 shows the average residual for each country when the equation
in Table 2.1 is applied to average 2014- 2016 data for the six variables
in that country. We combine these residuals with the estimate for life
evaluations in Dystopia so that the combined bar will always have
positive values. As can be seen in Figure 2.2, although some life
evaluation residuals are quite large, occasionally exceeding one point
on the scale from 0 to 10, they are always much smaller than the
calculated value in Dystopia, where the average life is rated at 1.85 on
the 0 to 10 scale.
What do the columns succeeding the Happiness Score(like
Family,Generosity, etc.) describe?
The following columns: GDP per Capita, Family, Life Expectancy,
Freedom, Generosity, Trust Government Corruption describe the extent to
which these factors contribute in evaluating the happiness in each
country. The Dystopia Residual metric actually is the Dystopia Happiness
Score(1.85) + the Residual value or the unexplained value for each
country as stated in the previous answer.
If you add all these factors up, you get the happiness score so it
might be un-reliable to model them to predict Happiness Scores.
d2015 <- read.csv("datasets/2015.csv")
d2016 <- read.csv("datasets/2016.csv")
d2017 <- read.csv("datasets/2017.csv")
d2018 <- read.csv("datasets/2018.csv")
d2019 <- read.csv("datasets/2019.csv")
Displaying the data
head(d2015)
Data frame is now printed using kable.
| Switzerland |
Western Europe |
1 |
7.587 |
0.03411 |
1.39651 |
1.34951 |
0.94143 |
0.66557 |
0.41978 |
0.29678 |
2.51738 |
| Iceland |
Western Europe |
2 |
7.561 |
0.04884 |
1.30232 |
1.40223 |
0.94784 |
0.62877 |
0.14145 |
0.43630 |
2.70201 |
| Denmark |
Western Europe |
3 |
7.527 |
0.03328 |
1.32548 |
1.36058 |
0.87464 |
0.64938 |
0.48357 |
0.34139 |
2.49204 |
| Norway |
Western Europe |
4 |
7.522 |
0.03880 |
1.45900 |
1.33095 |
0.88521 |
0.66973 |
0.36503 |
0.34699 |
2.46531 |
| Canada |
North America |
5 |
7.427 |
0.03553 |
1.32629 |
1.32261 |
0.90563 |
0.63297 |
0.32957 |
0.45811 |
2.45176 |
| Finland |
Western Europe |
6 |
7.406 |
0.03140 |
1.29025 |
1.31826 |
0.88911 |
0.64169 |
0.41372 |
0.23351 |
2.61955 |
head(d2016)
Data frame is now printed using kable.
| Denmark |
Western Europe |
1 |
7.526 |
7.460 |
7.592 |
1.44178 |
1.16374 |
0.79504 |
0.57941 |
0.44453 |
0.36171 |
2.73939 |
| Switzerland |
Western Europe |
2 |
7.509 |
7.428 |
7.590 |
1.52733 |
1.14524 |
0.86303 |
0.58557 |
0.41203 |
0.28083 |
2.69463 |
| Iceland |
Western Europe |
3 |
7.501 |
7.333 |
7.669 |
1.42666 |
1.18326 |
0.86733 |
0.56624 |
0.14975 |
0.47678 |
2.83137 |
| Norway |
Western Europe |
4 |
7.498 |
7.421 |
7.575 |
1.57744 |
1.12690 |
0.79579 |
0.59609 |
0.35776 |
0.37895 |
2.66465 |
| Finland |
Western Europe |
5 |
7.413 |
7.351 |
7.475 |
1.40598 |
1.13464 |
0.81091 |
0.57104 |
0.41004 |
0.25492 |
2.82596 |
| Canada |
North America |
6 |
7.404 |
7.335 |
7.473 |
1.44015 |
1.09610 |
0.82760 |
0.57370 |
0.31329 |
0.44834 |
2.70485 |
head(d2017)
Data frame is now printed using kable.
| Norway |
1 |
7.537 |
7.594445 |
7.479556 |
1.616463 |
1.533524 |
0.7966665 |
0.6354226 |
0.3620122 |
0.3159638 |
2.277027 |
| Denmark |
2 |
7.522 |
7.581728 |
7.462272 |
1.482383 |
1.551122 |
0.7925655 |
0.6260067 |
0.3552805 |
0.4007701 |
2.313707 |
| Iceland |
3 |
7.504 |
7.622031 |
7.385970 |
1.480633 |
1.610574 |
0.8335521 |
0.6271626 |
0.4755402 |
0.1535266 |
2.322715 |
| Switzerland |
4 |
7.494 |
7.561772 |
7.426228 |
1.564980 |
1.516912 |
0.8581313 |
0.6200706 |
0.2905493 |
0.3670073 |
2.276716 |
| Finland |
5 |
7.469 |
7.527542 |
7.410458 |
1.443572 |
1.540247 |
0.8091577 |
0.6179509 |
0.2454828 |
0.3826115 |
2.430182 |
| Netherlands |
6 |
7.377 |
7.427426 |
7.326574 |
1.503945 |
1.428939 |
0.8106961 |
0.5853845 |
0.4704898 |
0.2826618 |
2.294804 |
head(d2018)
Data frame is now printed using kable.
| 1 |
Finland |
7.632 |
1.305 |
1.592 |
0.874 |
0.681 |
0.202 |
0.393 |
| 2 |
Norway |
7.594 |
1.456 |
1.582 |
0.861 |
0.686 |
0.286 |
0.340 |
| 3 |
Denmark |
7.555 |
1.351 |
1.590 |
0.868 |
0.683 |
0.284 |
0.408 |
| 4 |
Iceland |
7.495 |
1.343 |
1.644 |
0.914 |
0.677 |
0.353 |
0.138 |
| 5 |
Switzerland |
7.487 |
1.420 |
1.549 |
0.927 |
0.660 |
0.256 |
0.357 |
| 6 |
Netherlands |
7.441 |
1.361 |
1.488 |
0.878 |
0.638 |
0.333 |
0.295 |
head(d2019)
Data frame is now printed using kable.
| 1 |
Finland |
7.769 |
1.340 |
1.587 |
0.986 |
0.596 |
0.153 |
0.393 |
| 2 |
Denmark |
7.600 |
1.383 |
1.573 |
0.996 |
0.592 |
0.252 |
0.410 |
| 3 |
Norway |
7.554 |
1.488 |
1.582 |
1.028 |
0.603 |
0.271 |
0.341 |
| 4 |
Iceland |
7.494 |
1.380 |
1.624 |
1.026 |
0.591 |
0.354 |
0.118 |
| 5 |
Netherlands |
7.488 |
1.396 |
1.522 |
0.999 |
0.557 |
0.322 |
0.298 |
| 6 |
Switzerland |
7.480 |
1.452 |
1.526 |
1.052 |
0.572 |
0.263 |
0.343 |
First 5 countries First let’s see the top 5 for the
years 2015, 2016. 2017, 2018, and 2019.
d2015[,c("Country", "Happiness.Rank", "Happiness.Score")] |> head(5)
## Country Happiness.Rank Happiness.Score
## 1 Switzerland 1 7.587
## 2 Iceland 2 7.561
## 3 Denmark 3 7.527
## 4 Norway 4 7.522
## 5 Canada 5 7.427
d2016[,c("Country", "Happiness.Rank", "Happiness.Score")] |> head(5)
## Country Happiness.Rank Happiness.Score
## 1 Denmark 1 7.526
## 2 Switzerland 2 7.509
## 3 Iceland 3 7.501
## 4 Norway 4 7.498
## 5 Finland 5 7.413
d2017[,c("Country", "Happiness.Rank", "Happiness.Score")] |> head(5)
## Country Happiness.Rank Happiness.Score
## 1 Norway 1 7.537
## 2 Denmark 2 7.522
## 3 Iceland 3 7.504
## 4 Switzerland 4 7.494
## 5 Finland 5 7.469
d2018[,c("Country.or.region", "Overall.rank", "Score")] |> head(5)
## Country.or.region Overall.rank Score
## 1 Finland 1 7.632
## 2 Norway 2 7.594
## 3 Denmark 3 7.555
## 4 Iceland 4 7.495
## 5 Switzerland 5 7.487
d2019[,c("Country.or.region", "Overall.rank", "Score")] |> head(5)
## Country.or.region Overall.rank Score
## 1 Finland 1 7.769
## 2 Denmark 2 7.600
## 3 Norway 3 7.554
## 4 Iceland 4 7.494
## 5 Netherlands 5 7.488
We can clearly see that the nordic countries like Finland, Norway and
Denkmare are constantly at the top.
Last 5
countries
Now let’s see at the 5 countries that score the
least.
d2015[,c("Country", "Happiness.Rank", "Happiness.Score")] |> tail(5)
## Country Happiness.Rank Happiness.Score
## 154 Rwanda 154 3.465
## 155 Benin 155 3.340
## 156 Syria 156 3.006
## 157 Burundi 157 2.905
## 158 Togo 158 2.839
d2016[,c("Country", "Happiness.Rank", "Happiness.Score")] |> tail(5)
## Country Happiness.Rank Happiness.Score
## 153 Benin 153 3.484
## 154 Afghanistan 154 3.360
## 155 Togo 155 3.303
## 156 Syria 156 3.069
## 157 Burundi 157 2.905
d2017[,c("Country", "Happiness.Rank", "Happiness.Score")] |> tail(5)
## Country Happiness.Rank Happiness.Score
## 151 Rwanda 151 3.471
## 152 Syria 152 3.462
## 153 Tanzania 153 3.349
## 154 Burundi 154 2.905
## 155 Central African Republic 155 2.693
d2018[,c("Country.or.region", "Overall.rank", "Score")] |> tail(5)
## Country.or.region Overall.rank Score
## 152 Yemen 152 3.355
## 153 Tanzania 153 3.303
## 154 South Sudan 154 3.254
## 155 Central African Republic 155 3.083
## 156 Burundi 156 2.905
d2019[,c("Country.or.region", "Overall.rank", "Score")] |> tail(5)
## Country.or.region Overall.rank Score
## 152 Rwanda 152 3.334
## 153 Tanzania 153 3.231
## 154 Afghanistan 154 3.203
## 155 Central African Republic 155 3.083
## 156 South Sudan 156 2.853
library('ggplot2')